Journal of Clinical and Translational Science

Peer reviewers provide a critical service to NIH by evaluating the scientific and technical merit of grant applications. While the tangible rewards for this service are limited, many reviewers feel review service makes them better applicants, improving their grant competitiveness. However, empirical evidence for this claim is limited. This study evaluates relationships between early career peer review service and subsequent application behavior and funding outcomes. Using NIH administrative data, applicants who served as Early Career Reviewers (ECRs) during the 2020 - 2021 council years were compared to a matched group of ECR-eligible applicants who had not served as reviewers (n=1,120 per group). To address non-random selection of ECRs, propensity score matching was used to balance groups on research field, demographics, productivity, career stage, and institutional resources. Outcomes, assessed over a three-year follow-up period, included submission volume, peer review scores, and funding outcomes for R01 and R01-equivalent applications. ECRs submitted more applications, were more likely to have their applications discussed, and were more likely to receive a high review score than matched controls. They were also more likely to receive R01 funding. While peer review scores do not solely determine award outcomes, these findings indicate that peer review service among ECRs is associated with improved grant application outcomes.

Objectives: Electronic Health Records (EHRs) impose a significant time burden on physicians, often requiring work to be completed outside of scheduled hours. While this burden is well-documented, how it evolves throughout emergency medicine (EM) residency remains poorly understood. This study aimed to quantify EHR usage patterns, analyze the composition of after-shift work, and characterize the development of EHR efficiency across EM training. Methods: We conducted a retrospective cohort study of EM residents (postgraduate year [PGY] 1-4) using 5.5 years of EHR audit log data (2020-2025) at a single academic institution. We analyzed EHR time per new patient encounter, stratified by postgraduate year, and categorized activities into domains such as documentation, chart review, and orders. EHR work was measured both during and after scheduled shifts. Results: The analysis included 144 unique residents and 167,010 new patient encounters across 15,386 shifts. Encounter-attributed EHR time per encounter decreased by 52% from PGY-1 to PGY-4 (median 19.9 to 9.6 minutes, p<0.001), despite an 86% increase in patient volume per shift (median 7 to 13 encounters). This efficiency gain was driven primarily by a 69% reduction in documentation time (9.3 to 2.9 minutes), accompanied by shorter notes. After-shift work (EHR activity after the 9-hour clinical shift) was present in 89.9-94.4% of encounters. At the shift level, combined after-shift EHR time (encounter-attributed plus tracking board) was a median of 64.2 minutes per shift for PGY-1 and 104.2 minutes for PGY-4. Shift-level tracking board activity dominated the after-shift burden and increased with training (median 40.2 to 79.0 minutes per shift from PGY-1 to PGY-4). Conclusions: EM residents achieve substantial gains in on-shift EHR efficiency, with the largest reductions observed in documentation time, accompanied by shorter notes and faster input speed. However, a persistent after-hours workload, dominated by administrative and patient flow tasks, suggests that (at least at this single institution) system-level factors--not just individual skill--may contribute to this pattern. Monitoring these objective EHR metrics may help programs identify struggling learners and evaluate the impact of interventions aimed at improving resident well-being and workflow efficiency.

Background: Early clinical exposure and student participation in research are important components of medical training. They may support learning motivation, evidence literacy, and self-directed learning. In many programmes, however, clinical training and research training remain separated. Few studies have explained, within a real teaching team, how learners turn clinical phenomena into researchable questions and how research participation can reshape their clinical understanding. Early Clinical and Research Training (ECART) is a clinical-research integration approach developed by an orthopaedic team at the Second Hospital of Shandong University. Methods: We conducted a theory-informed, interpretivist qualitative single-case study. The case was an orthopaedic clinical-research team at the Second Hospital of Shandong University. Participants included medical undergraduates, academic degree graduate students, professional degree graduate students, clinical teachers, and research platform leads. We used purposive sampling with maximum variation. Data were collected through semi-structured interviews and de-identified teaching documents. Data were analysed using the framework method and were interpreted with a Context-Activity-Mechanism-Outcome (CAMO) logic. Results: The analysis showed that ECART was not simply early entry into the clinic or early entry into the laboratory. It was a team-based learning process centred on real medical problems. Four themes were identified. First, early clinical exposure helped learners make real problems visible and nameable, rather than merely increasing exposure. Second, clinical-research connection followed different pathways. Professional degree graduate students often started from clinical uncertainties in residency training and case management, and moved toward evidence-informed small projects. Academic degree graduate students often started from literature gaps, experimental findings, and mechanistic hypotheses, and then used clinical feedback to calibrate meaning. Third, research training, through literature reading, group meetings, experimental design, data review, and mentor questioning, helped learners move from completing tasks to explaining problems. Fourth, sustained ECART depended on a tiered team ecology formed by clinical teachers, research mentors, research platforms, and senior peers. Based on these findings, we refined the ECART programme theory: real medical problems are translated through explanation, searching, experimentalisation, and feedback-based reinterpretation into research questions that learners can understand, discuss, and test. This process supports problem formation, evidence awareness, mechanistic reasoning, translational judgement, and career clarification. Conclusion: ECART is best understood as a clinical-research integrated learning ecology that emerges from real team practice, rather than as a fixed standardised course. Its educational value lies in a recurring cycle of real problems, research translation, multi-source feedback, and clinical reinterpretation. This framework may inform the design, evaluation, and contextual adaptation of clinical-research integration pathways in medical education.

ABSTRACT OBJECTIVES To examine whether psychological safety and power distance are associated with medical researchers' well-being, and whether these associations operate through team inclusiveness and conflict. DESIGN Cross-sectional survey study. SETTING A biomedical research institute at a major UK university. PARTICIPANTS 133 medical researchers from 17 teams, including 20 principal investigators and 113 team members. MAIN OUTCOME MEASURES Job satisfaction, life satisfaction, intrinsic motivation, and psychological detachment. Mediators were dimensions of team inclusiveness and team conflict. RESULTS Psychological safety had no significant direct associations with job satisfaction, life satisfaction, intrinsic motivation, or psychological detachment, but showed several indirect associations through researchers' team experiences. It was indirectly associated with higher job satisfaction, life satisfaction, and intrinsic motivation primarily through greater integration of differences, inclusion in decision making, or more constructive forms of conflict (bs=.23-.38, ps=.032-<.001).For psychological detachment, psychological safety showed conflicting indirect associations: it had the potential to support detachment through greater integration of differences and lower avoidant conflict (bs=.21-.56, ps=.054-.002), but to undermine detachment through greater inclusion in decision-making (b=-.26, p=.082). Power distance showed a different pattern. Most notably, it was positively associated with psychological detachment (b=.54, p=.062). However, power distance was indirectly associated with lower job satisfaction, life satisfaction, and intrinsic motivation, primarily through reduced integration of differences and greater dominating conflict (bs=-.14 to -.19, ps=.068-.020). CONCLUSIONS Common assumptions about psychological safety and power distance should be revisited. Psychological safety did not show strong direct benefits for researcher well-being, whereas power distance was not uniformly harmful and was positively associated with psychological detachment. A more nuanced understanding of both cultural dimensions is needed in medical research teams.

Objective: To estimate the adjusted relative risk (RR) of administrative grant disruption faced by first-time and mechanism-first principal investigators (PIs) during the 2025 National Institutes of Health (NIH) grant disruptions. Design: Retrospective cohort study linking NIH RePORTER data to a publicly curated registry of grants disrupted in 2025. Setting: All NIH active research grants in fiscal years 2024 to 2025. Participants: 80,976 active projects: 4,961 disrupted during the wave that peaked in May 2025, 76,015 non-disrupted controls. Main outcome measures: Adjusted RR of disruption by two pre-specified first-time PI constructs: absolute first-time PI (no prior NIH grant) and mechanism-first PI (no prior NIH grant with the same activity code). Modified Poisson regression with institution-clustered standard errors adjusted for project, institutional, and geographic covariates. A pre-specified fiscal year 2024 common-anchor analysis addressed year-of-disruption confounding. Results: Of 4,961 disrupted grants, 237 (4.8%) had an absolute first-time PI and 396 (8.0%) had a mechanism-first PI. After adjustment, absolute first-time PIs faced 77% elevated risk of disruption (RR 1.77, 95% CI 1.34 to 2.32) and mechanism-first PIs faced 57% elevated risk (RR 1.57, 1.16 to 2.11). Under the common-anchor analysis, the absolute first-time effect attenuated to RR 1.22 (0.95 to 1.58); the mechanism-first effect persisted (RR 1.48, 1.07 to 2.06). The elevated risk was concentrated in research-mechanism grants (RR 1.78, 1.26 to 2.52) and was robust across 8 of 9 pre-specified sensitivity analyses. The Track A start-time construct, which asks whether the disrupted project was the PI's debut grant, yielded null estimates (RR 0.98, 0.93 to 1.04), with any effect concentrated entirely in newly started projects. Conclusions: First-time and mechanism-first PIs faced disproportionately elevated risk of disruption during the 2025 NIH wave, concentrated in research-mechanism grants and robust to year-confounding-free identification. The relevant exposure was being early-career at the moment of administrative action, not at project initiation. The findings have direct implications for workforce equity in US biomedical research.

Abstract Objectives Value-Based Health Care (VBHC) increasingly guides health system redesign internationally. Despite the increasing availability of VBHC education, gaps remain between health professionals' conceptual understanding of VBHC and their confidence to implement it in practice. This study assessed perceived learning needs and preferences of healthcare professionals across foundational topics essential to VBHC implementation. Design Cross-sectional online survey study Setting and participants The survey was distributed to the global VBHC community and yielded 518 responses. Most respondents were based in the UK and Ireland (51%) and 65% had more than 10 years of experience in the health sector. Participants represented a variety of professional backgrounds, including clinicians (34%), operational or executive managers and leaders (22%), and life sciences or procurement professionals (13%). Primary and secondary outcome measures Primary outcome measures included self-reported interest and confidence across 15 VBHC domains and the magnitude of the gap between them. Secondary outcomes included perceived implementation challenges and preferred VBHC learning approaches, including prior engagement with VBHC-related learning. Results Respondents identified substantial VBHC implementation challenges, including implementing outcome measurement (62.4%), conflicting priorities (57.7%), and resistance to change (56.8%). Interest in all VBHC domains was high (median >= 80/10), while confidence to implement remained substantially lower across most domains (median <=50/100). The largest interest-confidence gaps were observed for reimbursement mechanisms, costing methodology, and overcoming implementation challenges. Interactive learning approaches, including in-person seminars/workshops (55.2%) and online masterclasses (53.9%) were preferred over self-directed formats. Conclusions This international survey identified consistent gaps between health professionals' interest in VBHC and their confidence to implement key VBHC domains in practice. Addressing these gaps through advanced, targeted and contextual education may support more effective and sustainable VBHC implementation in practice.

Introduction: A prominent symptom of post-acute sequelae of SARS-CoV-2 infection (i.e., Long COVID) is exercise intolerance with or without post-exertional malaise (PEM). PEM is characterized by the worsening of both symptoms and function following even minor physical or mental exertion, with symptoms typically worsening 12 to 48 hours after activity and lasting for days or even weeks. Individualized, supervised cardiopulmonary rehabilitation is considered a safe and effective intervention for many cardiac and pulmonary conditions, and has been effective in gradually improving function in previously hospitalized and nonhospitalized patients with severe COVID-19. While traditional cardiopulmonary rehabilitation approaches appear helpful in some situations, the exercise intolerance symptoms experienced by many individuals with Long COVID may require a different approach, especially when attempts to increase physical activity result in PEM. No clear consensus exists on the optimal treatment of PEM, and no major studies have evaluated the efficacy in individuals with Long COVID of either carefully supervised, individualized cardiopulmonary rehabilitation programs for exercise intolerance without significant PEM or activity pacing interventions designed to treat or prevent PEM. Methods and Analysis: The Researching COVID to Enhance Recovery Clinical Trials (RECOVER-CT) initiative funded by the National Institutes of Health (NIH) included a prospective, multicenter, randomized controlled platform trial (RECOVER-ENERGIZE) designed to assess two interventions in patients with Long COVID and exercise intolerance: (1) cardiopulmonary rehabilitation for patients without significant PEM and (2) structured activity pacing to prevent or reduce PEM in participants who experience the symptom. The intervention duration will be 12 weeks. The primary endpoints for the trial include the Endurance Shuttle Walk Test as a measure of endurance capacity for the cardiopulmonary rehabilitation intervention and a modified version of the DePaul Symptom Questionnaire - Post-Exertional Malaise for the pacing intervention. Assessments will be completed at baseline, middle of intervention, end of intervention, and 12 weeks after completion of the intervention, and include physical performance measures and patient-reported surveys. Ethics and Dissemination: The RECOVER-ENERGIZE trial protocol has been approved by an institutional review board (Advarra), and written informed consent will be obtained from all participants prior to enrollment. The trial is registered on ClinicalTrials.gov (NCT06404047). Formally assessing PEM and developing a structured activity pacing intervention delivered by local pacing coaches are novel features of this trial. Results will be disseminated through peer-reviewed publications, presentations at scientific conferences, and communication with participants, patient advocacy organizations, and the broader Long COVID community. De-identified participant data will be made available through the NIH RECOVER data repository in accordance with NIH data-sharing policies. If successful, this protocol will provide accessible tools that clinicians can use to address exercise intolerance and PEM in patients with Long COVID.

Background: Postgraduate medical residents frequently face difficulty in selecting appropriate statistical tests and preparing statistical analysis plans (SAPs) for thesis work. Existing resources often identify statistical tests without guiding implementation, reporting or software execution. Aims: To describe the development, features and content validation of ChooseMyStat, a free, open source, web based interactive tool for statistical test selection and SAP text generation in clinical research. Methods: ChooseMyStat was developed as a React based web application using an iterative, AI assisted development process under direct faculty supervision. The tool uses a branching decision algorithm covering 18 inferential statistical tests, two diagnostic accuracy measures, four agreement/reliability statistics, and four descriptive statistics scenarios. For each recommendation, it generates a SAP template paragraph, a results reporting example, step by step JASP instructions, and R code. Content validation was performed using 105 open-access original research articles from 15 broad medical specialties published in Indian journals during 2024 2025. Results: The tool covers commonly used statistical methods, including t tests, ANOVA, chi square variants, non parametric alternatives, correlation, regression (linear, logistic, ordinal), survival analysis, methods for clustered or repeated data, diagnostic accuracy measures, and agreement/reliability statistics. Among 365 statistical tests identified across 105 articles (excluding normality checking procedures), 346 (94.8%) were covered by the tool. Complete coverage of all statistical methods used was observed in 86 of 105 articles (81.9%). Conclusions: ChooseMyStat integrates statistical test selection with implementation guidance, SAP generation, reporting support and software instructions within a single interface. The tool may support postgraduate research training by improving accessibility to applied biostatistics guidance.

Importance: Several professional medical societies have removed race and ethnicity from widely used clinical algorithms with implications for millions of patients. Yet the opinions of patients and the public regarding the tensions underlying these pivotal changes have not been systematically explored. Objective: To assess global public opinion on the use of race or ethnicity in clinical algorithms, including preferences for different approaches to algorithmic reform and perceptions of alternative predictors. Design: Cross-sectional survey study. Setting: Multinational opt-in online survey conducted via Prolific in January 2026. Participants: A volunteer convenience sample with quota sampling to achieve approximately equal participation by sex at birth and across ten categories of self-identified race and ethnicity. Main Outcomes and Measures: Self-reported comfort with demographic and social predictors in clinical calculators, with net comfort defined as percentage extremely or somewhat comfortable minus percentage extremely or somewhat uncomfortable; preferences for race-specific versus race-free algorithms; perceptions of algorithmic harm or benefit. Results: Of 1,050 responses, 994 (94.7%) met eligibility criteria. Participants resided in 43 countries with a median age of 32.0 years (IQR, 26-41). Net comfort with the use of race or ethnicity in a hypothetical cancer risk calculator was +62.4% (95% CI: +57.8% to +66.9%), compared with +14.5% (95% CI: +9.1% to +19.9%) for postal or ZIP code. Overall, 87.9% (95% CI: 85.9% to 90.0%) were comfortable with race or ethnicity if a clinician explained its use and only 12.8% agreed race and ethnicity should never be used clinically. Across spirometry, kidney function, and cardiovascular risk calculators, 40.0% to 47.6% preferred race-specific versions, whereas 16.7% to 28.2% preferred race-free alternatives. Furthermore, a substantial proportion disagreed that they were well-represented by race and ethnicity categories, ranging from 22.1% for osteoporotic fracture risk equations to 42.9% for cardiovascular risk equations. These findings were consistent across countries, self-identified race and ethnicity, and among participants reporting prior experiences of racism in healthcare. Conclusions and Relevance: In our diverse multinational survey study, respondents were comfortable with the use of race and ethnicity across application areas, but often did not feel represented by existing categories and were less comfortable with the use of alternatives based on postal or ZIP codes.

Abstract Introduction In-hospital cardiac arrest carries high mortality despite standardized ACLS training. Educators face increasing time constraints in developing assessment tools for ACLS training. Two possible solutions to this problem are using pre-medical students or using artificial intelligence to generate test questions. This study compared the quality of pre-medical student-generated ACLS test questions vs. AI-generated ACLS test questions, testing the hypothesis that AI-generated questions are non-inferior to student-generated questions. Methods Ten pre-medical students created ACLS questions following predefined criteria, while an AI model (Northwell's Artificial Intelligence Hub) generated comparable questions. A blinded ACLS-certified physician evaluated questions on the qualities of Alignment, Clarity, Cognitive Level, and Question Design using a standardized rubric (Likert scale: 1 = poor quality, 5 = excellent). Student's T-test and Chi-square analysis were used to compare the quality of questions on different rubric domains within each arm (student vs. AI) and within one domain (eg, question Clarity) between arms. The Student's T test was used when 2 comparator groups were compared (eg, Clarity of student-generated vs. AI-generated questions) within one arm. The ANOVA test was used when comparing more than 2 comparator groups (eg, Alignment vs. Clarity vs. Cognitive Level) within one arm. Statistical significance was set as a priority at p <0.05. Results Both student-generated and AI-generated questions were of high quality. AI-generated questions achieved the maximum score in the domains of Alignment, Clarity, and Question Design, but fell short of perfect scores in the domain of Cognitive Level (8 of 50 questions were less than 5). Student-generated questions achieved less-than-perfect scores in each domain. No significant difference was found in overall mean question scores between groups (students = 4.79, AI = 4.81; p = 0.9). However, AI-generated questions had significantly-greater Clarity (students = 4.8, AI = 5; p = .0461), while Alignment, Cognitive level, and Question Design showed no significant differences. Conclusion AI-generated questions demonstrated overall quality comparable to those generated by pre-medical students, supporting the potential role of AI as a scalable tool in ACLS educational assessment development. Further studies are warranted to evaluate additional AI platforms and determine optimal integration of AI in medical education assessment design.

Background: Black and Latino adults in the United States experience a disproportionate burden of cardiometabolic conditions due to interacting behavioral, social, and structural drivers of health. Less is known about the impact of integrating digital health tools into CHW-led interventions to improve cardiometabolic health. This trial evaluates a multilevel community-digital health promotion model delivered by CHWs to improve service utilization, health behaviors and cardiometabolic health among Black and Latino adults. Methods: This community-partnered trial uses a randomized delayed-control group with a phased recruitment design. Four cohorts (N = 664) are enrolled through three community-based organizations (CBOs). Eligible participants are 18 years who self-identify as Black or Latino, and have prediabetes/diabetes, hypertension, or overweight/obesity. Participants are allocated to either (1) a multilevel intervention consisting of CBO and CHW capacity building combined with individualized CHW-led lifestyle coaching and group activities supported by digital tools, or (2) a delayed control group receiving SMS-only cardiometabolic health education. Data collected at baseline, 6, 9, and 18 months include surveys and health metrics. Qualitative data are collected from participants and community partners to assess intervention acceptability, implementation facilitators and barriers, and sustainability. Results: The primary outcome is health service utilization at 6 and 9 months. Secondary outcomes include health behaviors, health metrics, and social determinants of health. Sustainability of health behaviors and health metrics is assessed at 18 months. Conclusions: Findings will provide evidence to inform scalable, sustainable community-digital health models for CHW-supported cardiometabolic health interventions in underserved communities.

Participatory qualitative methods such as Photovoice are increasingly used to link research with social action. Recent advances in artificial intelligence (AI) may enhance data analysis, inference, and action planning within such participatory approaches. This study explored medical students' perceptions of social justice using conventional Photovoice analysis and assessed the potential contribution of generative AI (genAI). Nine students joined a six-week seminar, "Exploring the Concept of Social Justice Using Photovoice." An initial two-hour session covered ethics, the Photovoice framework, and photography techniques. Participants then captured images reflecting their views on social justice, wrote narratives, and engaged in guided group discussions. Human researchers and students conducted a three-stage Photovoice analysis: 1) selecting photographs, 2) contextualizing them with participant narratives, and 3) inductively coding themes. To explore how AI might support data analysis, the research team analyzed the same data with five generative tools including Sonix, ChatGPT, and Copilot. AI-generated themes and visual representations were compared with human-derived results for congruence, depth, and suggested action steps. Conventional analysis identified five major themes: (1) Social Justice and Inequality, (2) Contradictions and the Costs of Justice, (3) Community and Collective Action, (4) Environment and Environmental Justice, and (5) Perception, Subjectivity, and Perspective. AI-assisted analysis yielded six unified themes that closely aligned with human findings. Traditional Photovoice images conveyed authentic, lived experiences and strong emotional meaning, providing a powerful foundation for advocacy. AI-generated images and thematic summaries offered efficiency, creativity, and reduced researcher bias, improving generalizability. However, they lacked the emotional depth and contextual nuance present in participant-created visuals.

Abstract Background: Medical residents and physicians face persistently high-demand environments marked by heavy workloads, time pressure, night duties, and emotionally intense clinical encounters. These conditions increase vulnerability to stress-related outcomes, including burnout and impaired mental health, and can affect functioning at work. Personality traits are relatively stable individual differences and might help explain why some doctors experience greater stress vulnerability or use less adaptive coping strategies than others. Evidence linking the Big Five personality traits to stress, coping, and performance outcomes in residents and physicians has grown, but it remains difficult to apply because of inconsistent findings and heterogeneous measures. This systematic review and meta-analysis aim to synthesize the existing literature on how the Big Five personality traits influence these outcomes in medical residents and physicians. Methods: We conducted a PRISMA-aligned systematic review and meta-analysis (PROSPERO CRD42023483408). PubMed, Embase, MEDLINE (Ovid), Cochrane Library, Scopus, and Web of Science were searched from database inception to Nov 15, 2023. Searches were updated periodically through Jan 2026. Eligible studies were primary research in English involving medical residents and/or practicing physicians that assessed at least one Big Five trait using a recognized Five-Factor Model instrument and reported an association with a validated or clearly defined stress, coping, performance, or professional skills/aptitudes related outcome in medical residents or physicians. Studies exclusively involving medical students were excluded. Risk of bias was assessed using the AXIS tool (supplemented by Joanna Briggs Institute items) for cross-sectional studies and the Cochrane Risk of Bias 2 tool for the randomized trial. Where at least three comparable studies were available, effect sizes were pooled using random-effects models with restricted maximum likelihood estimation after Fishers z transformation; remaining studies were synthesized narratively. Results: Of the 4,967 records identified, 34 studies (21,379 participants) met the inclusion criteria; most were cross-sectional (30/34), with three longitudinal studies and one randomized trial. Meta-analyses were restricted to 11 studies reporting Maslach Burnout Inventory subscales and three studies reporting GHQ-12 psychological distress. Neuroticism showed the clearest and most consistent adverse associations: for emotional exhaustion (pooled r=0.418, 95% CI 0.219 to 0.616, p<0.001), for depersonalization (pooled r=0.304, 95% CI 0.166 to 0.442, p<0.001), and for personal accomplishment (pooled r=-0.244, 95% CI -0.393 to -0.094, p=0.005). Conscientiousness, Extraversion, and Agreeableness showed small protective patterns, with lower emotional exhaustion and depersonalization and higher personal accomplishment, although associations with stress were weak and generally non-significant. Openness showed a weaker, selective pattern, with lower depersonalization and higher personal accomplishment, but no clear association with emotional exhaustion or stress. Moderator analyses suggested that associations for Neuroticism, Conscientiousness, and Agreeableness varied more by experience and region than by specialty, whereas Extraversion was moderated mainly by experience; Openness showed little evidence of consistent moderation. Narrative synthesis of studies not included in the main meta-analyses was broadly concordant: Neuroticism was the most consistent vulnerability marker for burnout, distress, maladaptive coping, and poorer work-related outcomes, whereas Conscientiousness and, to a lesser extent, Extraversion were linked to more adaptive coping and more favorable performance-related indicators. Agreeableness showed modest prosocial and attitudinal benefits, and Openness remained the least consistent trait across outcomes. Overall risk of bias was low to moderate for most observational studies, although heterogeneity was substantial across pooled analyses. Conclusions: Big Five personality traits have modest correlation with physicians and residents burnout, distress, coping, and work-related functioning. Neuroticism emerged as the clearest vulnerability marker, whereas Conscientiousness and, to a lesser extent, Extraversion and Agreeableness showed small protective associations. Interpretation is limited by the predominance of cross-sectional designs, reliance on self-report, substantial heterogeneity, and restricted geographic representation. These findings support the use of personality traits as a supportive and formative resource within medical education and workforce well-being, but not for deterministic selection or labelling. Larger longitudinal and intervention studies using multi-method outcomes are needed to clarify mechanisms and causal pathways.

Introduction Shadowing, as an educational method based on active observation, can foster a realistic understanding of professional roles and enhance the communication skills of medical students. This study aimed to design, implement, and evaluate a shadowing program for basic sciences medical students. Methods This development study was conducted based on the ADDIE model in five phases. The study population consisted of 799 medical students in semesters 2 to 5. The stages included Analysis (determining needs through literature review and expert panels), Design (specifying learning environments and evaluation methods), Development (preparing guides and educational tools), Implementation (within the Medical Ethics course), and Evaluation (using questionnaires and reflection forms). Findings This study aimed to design and evaluate an educational shadowing program based on the ADDIE model. In the Analysis phase, the profiles of 799 students and learning objectives were determined. In the Design phase, a structured program for four types of shadowing was designed. In the Development phase, all guides and educational tools were prepared. In the Implementation phase, the program was carried out with complete coverage and adherence to ethical considerations. Finally, the program evaluation showed that "Motivation to become a good physician" (3.75-3.95) and "Enhancing empathy" (3.50-3.94) received the highest scores, while "Increasing understanding of the basic science-clinical connection" (2.53-2.89) and "Willingness to attend on holidays" (1.87-2.31) received the lowest scores. Conclusion The findings indicate that implementing the shadowing program is an effective method for strengthening the professional attitudes and academic motivation of medical students. However, the program did not significantly improve students perception of the basic science-clinical connection, indicating a need for curricular refinement. The continuation and extension of this program to other levels and fields of medical sciences are recommended.

Background: Collaborative research programs increasingly require infrastructure capable of integrating heterogeneous participant, sample, and experimental data while meeting evolving research needs. Existing tools, including clinical EHRs, REDCap, generic research information management systems, and bespoke database builds, were not designed to operationalize project-specific data models. The Institute for Clinical and Translational Science (ICTS) at the University of California, Irvine (UCI) ICTS-Dashboard fills this need by providing a general purpose research information management system. Methods: We describe the ICTS-Dashboard, built as an open-source, schema-driven platform in which database structure, server-side validation, representational state transfer application programming interfaces (REST APIs), web-based forms, and reproducible exports are all generated from a single versioned java script object notation (JSON) Schema set. The backend is implemented in Django, Django REST Framework, and PostgreSQL; the frontend in React. We instantiate the platform with the Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Data Model and extend it with two case studies: a locally developed biobank table for biospecimen logistics, and an embedded adaptation of the RAG-HPO retrieval-augmented phenotype curation tool. Results: The ICTS-Dashboard deployed at the UCI-GREGoR site supports 37 schema-derived tables and 250 documented API endpoints. It holds metadata for 2,558 participants, 1,237 families, 5,517 biobank entries, 2,466 sequenced biospecimens, and 289 genetic findings, and supports quarterly external data submissions regenerated directly from the database. The biobank extension adds entities the consortium does not standardize while preserving foreign-key linkage to rare disease records; the RAG-HPO module adds curator-mediated phenotype normalization against 19,389 indexed HPO terms. Both were integrated without modifying the GREGoR data model. Conclusion: A version-controlled, machine-readable data model can serve not only as a data sharing standard but as the operational backbone of a research program when paired with schema-governed tooling. The Dashboard's architecture is not intrinsic to a data model or to rare disease; any collaborative research program with a structured, versioned model can adopt the same pattern to reduce implementation overhead and improve reproducibility, harmonization, and findable, accessible, interoperable, and reproducible (FAIR)-aligned accessibility.

Background: Health-related social needs (HRSNs), particularly housing instability, are significant drivers of poor health outcomes among Medicaid populations. New York State's Social Care Networks (SCNs) aim to systematically connect members to housing services through coordinated referral systems. However, limited systematic analysis of referral patterns hinders quality improvement efforts. We analyzed housing referral outcomes and workflows to identify barriers to successful service connections. Methods: We conducted a mixed-methods quality improvement study at Public Health Solutions' WholeYouNYC SCN Coordination Center. Quantitative analysis examined 4,258 housing referrals submitted between June 2025 and January 2026, extracted from the Unite Us platform via Power BI dashboard. We calculated acceptance rates, analyzed time metrics, and examined outcomes by receiving organization. Qualitative data were collected through structured consultations with 7 staff members (5 navigators, 2 supervisors) and review of internal workflow documentation. Process mapping identified workflow bottlenecks. Results: Of 4,258 housing referrals, only 45% (n=1,936) were accepted by receiving organizations, while 19% (n=815) were rejected and 32% (n=1,382) remained awaiting response with no recorded action. Average time to acceptance was 8 days for accepted referrals. Acceptance rates were consistent across top receiving organizations (44-46%), suggesting systemic rather than partner-specific barriers. Analysis of unresolved referrals revealed prolonged cases, with the longest pending 271 days. Three critical workflow bottlenecks were identified: CBO response delays, missing housing documentation, and challenges with client engagement. Conclusions: Low housing connection rates (45%) and prolonged unresolved referrals (up to 271 days) indicate systemic barriers requiring interventions at multiple levels. Recommendations include establishing CBO response time benchmarks, implementing automated follow-up protocols, standardizing documentation requirements, and enhancing real-time data monitoring. These findings provide an evidence-based framework for quality improvement in social care coordination programs.

Although large language models (LLMs) have shown promise for discharge summary generation, their value may be greater in longer hospitalizations, where increasing documentation volume and complexity increase both clinician burden and the risk of communication failures during transitions of care. Prior evaluations of LLM-generated discharge summaries have largely involved shorter stays and have rarely examined receiving-clinician priorities or incidental finding reporting. We compared LLM-generated and human-authored discharge summaries for 60 Internal Medicine hospitalizations lasting 7 to 21 days, with paired assessment by hospitalists and primary care physicians (PCPs). Clinician reviewers preferred LLM-generated summaries for 95% of encounters and rated them higher for quality, readability, factuality and completeness. PCPs, the primary recipients responsible for post-discharge care, found that LLM-generated summaries were better for understanding and communicating hospital care to patients, and providing follow-up care. LLM-generated summaries had fewer annotated errors, primarily due to fewer omissions, without increased estimated harm potential or likelihood compared with human-authored summaries. Benefits of LLM-generated summaries were especially salient for PCPs, who identified more omissions with greater downstream likelihood of harm than hospitalists. This underscores the importance of designing transition documents around the needs of clinicians assuming care post-discharge. LLM identification of radiology incidental findings was generally accurate and appropriate, suggesting potential to improve follow-up of clinically relevant findings. These findings extend prior work by demonstrating clinical value of LLMs in summarizing longer, complex hospitalizations and highlighting the value of stakeholder-centered design in clinical AI systems. Together, they support supervised LLM-assisted discharge summarization as a tool to reduce cognitive burden, improve documentation quality, and enhance transition-of-care communication.

Introduction: Menstrual cycle phase has been proposed as a source of intra-individual variability in resting energy expenditure and the thermic effect of food in premenopausal females, yet studies examining the thermic effect of food across menstrual cycle phases report conflicting findings. Methods: This protocol describes a secondary analysis of prespecified outcomes from a non-randomized, two-period crossover trial primarily designed to assess postprandial plasma triglyceride concentrations across menstrual cycle phases (ClinicalTrials.gov: NCT07459465) in 12 premenopausal females aged 18-30 years, free of chronic disease and hormonal contraceptive use, recruited in Ottawa, Canada. Participants complete two experimental sessions: one in the early follicular phase and one in the mid-luteal phase, each involving consumption of a high-fat meal. Eleven secondary outcomes will be reported: fasting resting energy expenditure, thermic effect of food, respiratory exchange ratio, carbohydrate oxidation rate, lipid oxidation rate, desire to eat, hunger, fullness, prospective food consumption, serum beta-estradiol, and serum progesterone. Masked outcome analyses are performed using linear mixed-effects models. Results: Recruitment began on 26 March 2026; results will be reported in the Stage 2 manuscript. Discussion: Findings from this trial may help clarify whether menstrual cycle phase constitutes a meaningful source of intra-individual variability in energy metabolism, with implications for the design of metabolic research in premenopausal females.

Background. Complex cases in specialized pediatric care require consistent adherence to evidence-based clinical pathways and protocols to ensure safe, high-quality, and equitable care. Currently, clinical pathways and supporting documentation are frequently distributed across multiple platforms, leading to fragmentation. Human-centered design principles can guide the development of healthcare technologies that minimize cognitive load and support rapid, efficient access to relevant information in clinical settings. The purpose of this study is to design and evaluate perceived usability of a pediatric cardiac center digital guideline management system that is embedded within the electronic health record leveraging human-centered design. Methods. This study used a mixed-methods usability evaluation to assess a digital guideline management system prototype embedded into clinical workflow. Through human-centered design principles, the prototype provides a centralized digital document library that organizes cardiac-specific clinical pathways, guidelines, procedures, and related resources. A small but diverse sample, encompassing a wide variety of roles and clinical areas within the pediatric cardiac center, was recruited to evaluate the perceived usability of the prototype. Usability was evaluated by stakeholders using the validated System Usability Scale (SUS) with additional optional questions to understand perceptions of the information architecture and clinical value. Results. Preliminary usability testing showed a mean SUS composite score of 76.5, indicating above average usability. Questions related to the complexity of the system and user confidence received high scores across participants. Lower scores were observed for questions related to usage frequency and ability to learn the system very quickly. Conclusion. Leveraging human-centered design when building a digital guideline management system embedded within clinical workflow revealed positive perception from participants. By centralizing access to clinical resources, this prototype can reduce current-state fragmentation. Further evaluation of larger samples is needed to develop a list of future recommendations.

Background: Black patients and individuals with low socioeconomic status (SES) face significant disparities in accessing curative therapies for hepatocellular carcinoma (HCC), including liver transplantation. This study aimed to develop provider-co-created intervention prototypes in response to patient-identified barriers and recommendations. Methods: A human-centered design session with hepatology and transplant providers at a large academic medical center was conducted. Prior to the session, participants were presented with barriers and preliminary solutions identified through an earlier human-centered design session with Black and low-SES patients. Using structured ideation methods, including brainwriting, challenge mapping, and concept voting, providers co-created intervention prototypes. Final concepts were synthesized from patient insights, provider input, and design methods using affinity diagramming and concept modeling. Results: Nine providers participated in the session. They focused on three key areas for intervention: inefficiencies in transplant pre-evaluation, inadequate social support, and information overload. Solutions included: (1) a structured triage pathway to standardize referrals and reduce delays; (2) a peer navigator model to guide patients through the transplant process; and (3) a multimodal transplant education roadmap to improve comprehension and engagement. These prototypes addressed both patient- and system-level barriers. Conclusions: Protypes developed through provider-led design, grounded in patient-identified barriers and co-created ideas, can yield actionable, scalable strategies to advance equity in HCC care. Future work will refine these prototypes through patient feedback and pilot them in clinical settings.